{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c3b3d6d6-629d-4f6b-926c-6d80e0e046d9",
   "metadata": {},
   "source": [
    "## 不加normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b5871fd6-0569-414b-b394-567073a9dbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                            | 0/2 [00:00<?, ?it/s]C:\\Users\\wangyifan\\AppData\\Local\\Temp\\ipykernel_6492\\563157899.py:44: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  x_layer = F.softmax(x_layer)\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:03<00:00,  1.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time is 0:00:03.150172 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1876 0.55458397\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确率:0.9986\n"
     ]
    }
   ],
   "source": [
    "#1、导入包和定义参数\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as tud\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import trange\n",
    "EPOCHES = 2\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#2、定义数据，这里是自己建立\n",
    "torch.manual_seed(1)\n",
    "n_data = torch.ones(10000, 2)     #内容为一个 100 行 2 列 的 tensor\n",
    "x0 = torch.normal(3*n_data, 1)  \n",
    "y0 = torch.zeros(10000)  \n",
    "x1 = torch.normal(-2*n_data, 1)  \n",
    "y1 = torch.ones(10000)\n",
    "x2 = torch.normal(10*n_data, 1)\n",
    "y2 = torch.ones(10000)*2\n",
    "x = torch.cat((x0, x1,x2)).type(torch.FloatTensor)  \n",
    "y = torch.cat((y0, y1,y2)).type(torch.LongTensor) \n",
    "\n",
    "torch_dataset = tud.TensorDataset(x,y)\n",
    "loader = tud.DataLoader(\n",
    "    dataset=torch_dataset,\n",
    "    batch_size=32,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "if USE_CUDA:\n",
    "    x = x.cuda()\n",
    "    y = y.cuda()\n",
    "    \n",
    "#3、定义网络和模型 两层的网络\n",
    "class Net(torch.nn.Module):  \n",
    "    def __init__(self, n_feature, n_hidden, n_output):  \n",
    "        super(Net, self).__init__()  \n",
    "        self.n_hidden = nn.Linear(n_feature, n_hidden) \n",
    "        self.out = nn.Linear(n_hidden, n_output)  \n",
    "    def forward(self, x_layer):  \n",
    "        x_layer = torch.relu(self.n_hidden(x_layer))  \n",
    "        x_layer = self.out(x_layer) \n",
    "        x_layer = F.softmax(x_layer)  \n",
    "        return x_layer  \n",
    "        # 定义权值初始化\n",
    "    def initialize_weights(self):\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                torch.nn.init.xavier_uniform_(m.weight.data)\n",
    "                if m.bias is not None:\n",
    "                    m.bias.data.zero_()\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                m.weight.data.fill_(1)\n",
    "                m.bias.data.zero_()\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                torch.nn.init.normal_(m.weight.data, 0, 0.001)\n",
    "                m.bias.data.zero_()\n",
    "                \n",
    "\n",
    "model = Net(n_feature=2, n_hidden=20, n_output=3)  \n",
    "if USE_CUDA:\n",
    "    model = model.cuda()\n",
    "# print(net)  \n",
    "# optimizer = torch.optim.SGD(model.parameters(), lr=0.02)  \n",
    "# loss_func = torch.nn.CrossEntropyLoss() \n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)  \n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)     # 设置学习率下降策略\n",
    "loss_func =  nn.CrossEntropyLoss() \n",
    "\n",
    "\n",
    "# 4、 完成训练和迭代\n",
    "from datetime import  datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "losses = []\n",
    "for i in trange(EPOCHES):\n",
    "    for step, (batch_x,batch_y) in enumerate(loader):\n",
    "        x_b = batch_x\n",
    "        y_b = batch_y\n",
    "        if USE_CUDA:\n",
    "            x_b = x_b.cuda()\n",
    "            y_b = y_b.cuda()\n",
    "        out = model(x_b)  \n",
    "    # print(out.shape, y.shape)  \n",
    "        loss = loss_func(out, y_b) \n",
    "        losses.append(loss.cpu().detach().numpy())\n",
    "        optimizer.zero_grad()  \n",
    "        loss.backward()  \n",
    "        optimizer.step() \n",
    "\n",
    "end_time = datetime.now()       \n",
    "msecs = (end_time - start_time) \n",
    "print(f\"time is {msecs} s\" )\n",
    "\n",
    "#5、其它--打印损失\n",
    "x1 = range(0,len(losses))\n",
    "plt.plot(x1,losses,\".-\")\n",
    "plt.xlabel(\"epoches\")\n",
    "plt.ylabel(\"test loss\")\n",
    "plt.show()\n",
    "\n",
    "#打印下损失\n",
    "print(len(losses),losses[-1])\n",
    "\n",
    "#6、其它--结果打印 \n",
    "train_result = model(x)  \n",
    "\n",
    "# print(train_result.shape)  \n",
    "train_predict = torch.max(train_result, 1)[1]  \n",
    "x_  = x.data.cpu().numpy()\n",
    "plt.scatter(x_[:, 0],x_[:, 1], c=train_predict.cpu().data.numpy(), s=100, lw=0, cmap='RdYlGn')  \n",
    "plt.show() \n",
    "\n",
    "acc = accuracy_score(train_predict.cpu().numpy(),y.cpu().numpy())\n",
    "print(f\"正确率:{acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "103e5318-bcaa-4f16-82eb-89d42435fbdd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4f0bd2a8-014a-4c90-926b-34ae483578d4",
   "metadata": {},
   "source": [
    "## 加入normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2650e4cc-9b07-43bb-ac1a-8d9d52c244fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                            | 0/2 [00:00<?, ?it/s]C:\\Users\\wangyifan\\AppData\\Local\\Temp\\ipykernel_6492\\3319725330.py:14: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  x_layer = F.softmax(x_layer)\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:04<00:00,  2.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time is 0:00:04.177674 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1876 0.55195844\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确率:0.9999333333333333\n"
     ]
    }
   ],
   "source": [
    "#3、定义网络和模型 两层的网络\n",
    "class Net(torch.nn.Module):  \n",
    "    def __init__(self, n_feature, n_hidden, n_output):  \n",
    "        super(Net, self).__init__()  \n",
    "        self.n_hidden = nn.Linear(n_feature, n_hidden) \n",
    "#         self.dropout = nn.Dropout(0.1)\n",
    "        self.bnm = nn.BatchNorm1d(n_hidden)\n",
    "        self.out = nn.Linear(n_hidden, n_output)  \n",
    "    def forward(self, x_layer):  \n",
    "        x_layer = torch.relu(self.n_hidden(x_layer))  \n",
    "#         x_layer = self.dropout(x_layer)\n",
    "        x_layer = self.bnm(x_layer)\n",
    "        x_layer = self.out(x_layer) \n",
    "        x_layer = F.softmax(x_layer)  \n",
    "        return x_layer  \n",
    "        # 定义权值初始化\n",
    "    def initialize_weights(self):\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                torch.nn.init.xavier_uniform_(m.weight.data)\n",
    "                if m.bias is not None:\n",
    "                    m.bias.data.zero_()\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                m.weight.data.fill_(1)\n",
    "                m.bias.data.zero_()\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                torch.nn.init.normal_(m.weight.data, 0, 0.001)\n",
    "                m.bias.data.zero_()\n",
    "                \n",
    "model = Net(n_feature=2, n_hidden=20, n_output=3)  \n",
    "model.initialize_weights()    # 初始化权值\n",
    "\n",
    "if USE_CUDA:\n",
    "    model = model.cuda()\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)  \n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)     # 设置学习率下降策略\n",
    "loss_func =  nn.CrossEntropyLoss() \n",
    "\n",
    "# 4、 完成训练和迭代\n",
    "from datetime import  datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "losses = []\n",
    "for i in trange(EPOCHES):\n",
    "    for step, (batch_x,batch_y) in enumerate(loader):\n",
    "        x_b = batch_x\n",
    "        y_b = batch_y\n",
    "        if USE_CUDA:\n",
    "            x_b = x_b.cuda()\n",
    "            y_b = y_b.cuda()\n",
    "        out = model(x_b)  \n",
    "    # print(out.shape, y.shape)  \n",
    "        loss = loss_func(out, y_b) \n",
    "        losses.append(loss.cpu().detach().numpy())\n",
    "        optimizer.zero_grad()  \n",
    "        loss.backward()  \n",
    "        optimizer.step() \n",
    "\n",
    "end_time = datetime.now()       \n",
    "msecs = (end_time - start_time) \n",
    "print(f\"time is {msecs} s\" )\n",
    "\n",
    "#5、其它--打印损失\n",
    "x1 = range(0,len(losses))\n",
    "plt.plot(x1,losses,\".-\")\n",
    "plt.xlabel(\"epoches\")\n",
    "plt.ylabel(\"test loss\")\n",
    "plt.show()\n",
    "\n",
    "#打印下损失\n",
    "print(len(losses),losses[-1])\n",
    "\n",
    "#6、其它--结果打印 \n",
    "train_result = model(x)  \n",
    "\n",
    "# print(train_result.shape)  \n",
    "train_predict = torch.max(train_result, 1)[1]  \n",
    "x_  = x.data.cpu().numpy()\n",
    "plt.scatter(x_[:, 0],x_[:, 1], c=train_predict.cpu().data.numpy(), s=100, lw=0, cmap='RdYlGn')  \n",
    "plt.show() \n",
    "\n",
    "acc = accuracy_score(train_predict.cpu().numpy(),y.cpu().numpy())\n",
    "print(f\"正确率:{acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91dbc3b5-307a-46fe-8f19-8599846b7edb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "144193b2-b074-4f9d-a6c8-0aaf3ea4cece",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5a3fb9ed-4b6a-4cf8-b7c2-781cf0f21ea0",
   "metadata": {},
   "source": [
    "## 其他测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab50c1b2-6b89-4eeb-a664-c190a876ba07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                            | 0/2 [00:00<?, ?it/s]C:\\Users\\wangyifan\\AppData\\Local\\Temp\\ipykernel_6492\\4048321076.py:50: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  x_layer = F.softmax(x_layer)\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time is 0:00:02.367932 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "938 0.84670657\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确率:0.6665666666666666\n"
     ]
    }
   ],
   "source": [
    "#1、导入包和定义参数\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as tud\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import trange\n",
    "EPOCHES = 2\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#2、定义数据，这里是自己建立\n",
    "torch.manual_seed(1)\n",
    "n_data = torch.ones(10000, 2)     #内容为一个 100 行 2 列 的 tensor\n",
    "x0 = torch.normal(3*n_data, 1)  \n",
    "y0 = torch.zeros(10000)  \n",
    "x1 = torch.normal(-2*n_data, 1)  \n",
    "y1 = torch.ones(10000)\n",
    "x2 = torch.normal(10*n_data, 1)\n",
    "y2 = torch.ones(10000)*2\n",
    "x = torch.cat((x0, x1,x2)).type(torch.FloatTensor)  \n",
    "y = torch.cat((y0, y1,y2)).type(torch.LongTensor) \n",
    "\n",
    "torch_dataset = tud.TensorDataset(x,y)\n",
    "loader = tud.DataLoader(\n",
    "    dataset=torch_dataset,\n",
    "    batch_size=64,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "if USE_CUDA:\n",
    "    x = x.cuda()\n",
    "    y = y.cuda()\n",
    "    \n",
    "#3、定义网络和模型 两层的网络\n",
    "class Net(torch.nn.Module):  \n",
    "    def __init__(self, n_feature, n_hidden, n_output):  \n",
    "        super(Net, self).__init__()  \n",
    "        self.n_hidden = nn.Linear(n_feature, n_hidden) \n",
    "#         self.dropout = nn.Dropout(0.1)\n",
    "        #self.bnm = nn.BatchNorm1d(n_hidden)\n",
    "        self.nm = nn.LayerNorm(n_hidden)\n",
    "        self.out = nn.Linear(n_hidden, n_output)  \n",
    "    def forward(self, x_layer):  \n",
    "        x_layer = torch.relu(self.n_hidden(x_layer))  \n",
    "#         x_layer = self.dropout(x_layer)\n",
    "        #x_layer = self.bnm(x_layer)\n",
    "        x_layer = self.nm(x_layer)\n",
    "        x_layer = self.out(x_layer) \n",
    "        x_layer = F.softmax(x_layer)  \n",
    "        return x_layer  \n",
    "        # 定义权值初始化\n",
    "    def initialize_weights(self):\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                torch.nn.init.xavier_uniform_(m.weight.data)\n",
    "                if m.bias is not None:\n",
    "                    m.bias.data.zero_()\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                m.weight.data.fill_(1)\n",
    "                m.bias.data.zero_()\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                torch.nn.init.normal_(m.weight.data, 0, 0.001)\n",
    "                m.bias.data.zero_()\n",
    "                \n",
    "model = Net(n_feature=2, n_hidden=20, n_output=3)  \n",
    "model.initialize_weights()    # 初始化权值\n",
    "\n",
    "if USE_CUDA:\n",
    "    model = model.cuda()\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)  \n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)     # 设置学习率下降策略\n",
    "loss_func =  nn.CrossEntropyLoss() \n",
    "\n",
    "# 4、 完成训练和迭代\n",
    "from datetime import  datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "losses = []\n",
    "for i in trange(EPOCHES):\n",
    "    for step, (batch_x,batch_y) in enumerate(loader):\n",
    "        x_b = batch_x\n",
    "        y_b = batch_y\n",
    "        if USE_CUDA:\n",
    "            x_b = x_b.cuda()\n",
    "            y_b = y_b.cuda()\n",
    "        out = model(x_b)  \n",
    "    # print(out.shape, y.shape)  \n",
    "        loss = loss_func(out, y_b) \n",
    "        losses.append(loss.cpu().detach().numpy())\n",
    "        optimizer.zero_grad()  \n",
    "        loss.backward()  \n",
    "        optimizer.step() \n",
    "\n",
    "end_time = datetime.now()       \n",
    "msecs = (end_time - start_time) \n",
    "print(f\"time is {msecs} s\" )\n",
    "\n",
    "#5、其它--打印损失\n",
    "x1 = range(0,len(losses))\n",
    "plt.plot(x1,losses,\".-\")\n",
    "plt.xlabel(\"epoches\")\n",
    "plt.ylabel(\"test loss\")\n",
    "plt.show()\n",
    "\n",
    "#打印下损失\n",
    "print(len(losses),losses[-1])\n",
    "\n",
    "#6、其它--结果打印 \n",
    "train_result = model(x)  \n",
    "\n",
    "# print(train_result.shape)  \n",
    "train_predict = torch.max(train_result, 1)[1]  \n",
    "x_  = x.data.cpu().numpy()\n",
    "plt.scatter(x_[:, 0],x_[:, 1], c=train_predict.cpu().data.numpy(), s=100, lw=0, cmap='RdYlGn')  \n",
    "plt.show() \n",
    "\n",
    "acc = accuracy_score(train_predict.cpu().numpy(),y.cpu().numpy())\n",
    "print(f\"正确率:{acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9e81637-ae23-4926-8d49-614bf390842a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
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